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Data X:
149 96 18 68 86 13 152 75 7 55 62 7 139 70 31 39 70 12 148 88 39 32 71 13 158 114 46 62 108 7 128 69 31 33 64 7 224 176 67 52 119 13 159 114 35 62 97 15 105 121 52 77 129 13 159 110 77 76 153 11 167 158 37 41 78 8 165 116 32 48 80 11 159 181 36 63 99 6 119 77 38 30 68 11 176 141 69 78 147 12 54 35 21 19 40 6 91 80 26 31 57 11 163 152 54 66 120 12 124 97 36 35 71 9 137 99 42 42 84 10 121 84 23 45 68 10 153 68 34 21 55 6 148 101 112 25 137 14 221 107 35 44 79 11 188 88 47 69 116 14 149 112 47 54 101 12 244 171 37 74 111 11 148 137 109 80 189 16 92 77 24 42 66 13 150 66 20 61 81 10 153 93 22 41 63 12 94 105 23 46 69 8 156 131 32 39 71 12 146 89 7 63 70 6 132 102 30 34 64 9 161 161 92 51 143 10 105 120 43 42 85 11 97 127 55 31 86 10 151 77 16 39 55 10 131 108 49 20 69 13 166 85 71 49 120 9 157 168 43 53 96 12 111 48 29 31 60 6 145 152 56 39 95 11 162 75 46 54 100 13 163 107 19 49 68 11 59 62 23 34 57 12 187 121 59 46 105 11 109 124 30 55 85 12 90 72 61 42 103 8 105 40 7 50 57 13 83 58 38 13 51 12 116 97 32 37 69 12 42 88 16 25 41 7 148 126 19 30 49 11 155 104 22 28 50 12 125 148 48 45 93 13 116 146 23 35 58 10 128 80 26 28 54 6 138 97 33 41 74 14 49 25 9 6 15 8 96 99 24 45 69 13 164 118 34 73 107 9 162 58 48 17 65 13 99 63 18 40 58 8 202 139 43 64 107 16 186 50 33 37 70 9 66 60 28 25 53 9 183 152 71 65 136 11 214 142 26 100 126 13 188 94 67 28 95 15 104 66 34 35 69 12 177 127 80 56 136 12 126 67 29 29 58 11 76 90 16 43 59 9 99 75 59 59 118 15 157 96 58 52 110 7 139 128 32 50 82 13 78 41 47 3 50 NA 162 146 43 59 102 13 108 69 38 27 65 13 159 186 29 61 90 13 74 81 36 28 64 10 110 85 32 51 83 8 96 54 35 35 70 11 116 46 21 29 50 13 87 106 29 48 77 11 97 34 12 25 37 4 127 60 37 44 81 10 106 95 37 64 101 12 80 57 47 32 79 11 74 62 51 20 71 11 91 36 32 28 60 9 133 56 21 34 55 13 74 54 13 31 44 13 114 64 14 26 40 6 140 76 -2 58 56 10 95 98 20 23 43 9 98 88 24 21 45 8 121 35 11 21 32 9 126 102 23 33 56 7 98 61 24 16 40 11 95 80 14 20 34 14 110 49 52 37 89 8 70 78 15 35 50 11 102 90 23 33 56 10 86 45 19 27 46 8 130 55 35 41 76 10 96 96 24 40 64 14 102 43 39 35 74 9 100 52 29 28 57 14 94 60 13 32 45 5 52 54 8 22 30 6 98 51 18 44 62 10 118 51 24 27 51 12 99 38 19 17 36 11 48 41 23 12 34 4 50 146 16 45 61 13 150 182 33 37 70 18 154 192 32 37 69 18 109 263 37 108 145 17 68 35 14 10 23 13 194 439 52 68 120 17 158 214 75 72 147 19 159 341 72 143 215 16 67 58 15 9 24 13 147 292 29 55 84 18 39 85 13 17 30 15 100 200 40 37 77 11 111 158 19 27 46 13 138 199 24 37 61 16 101 297 121 58 178 14 131 227 93 66 160 19 101 108 36 21 57 14 114 86 23 19 42 15 165 302 85 78 163 16 114 148 41 35 75 15 111 178 46 48 94 15 75 120 18 27 45 12 82 207 35 43 78 13 121 157 17 30 47 17 32 128 4 25 29 9 150 296 28 69 97 18 117 323 44 72 116 16 71 79 10 23 32 12 165 70 38 13 50 18 154 146 57 61 118 15 126 246 23 43 66 18 138 145 26 22 48 16 149 196 36 51 86 16 145 199 22 67 89 18 120 127 40 36 76 14 138 91 18 21 39 12 109 153 31 44 75 14 132 299 11 45 57 15 172 228 38 34 72 18 169 190 24 36 60 10 114 180 37 72 109 16 156 212 37 39 76 18 172 269 22 43 65 17 68 130 15 25 40 15 89 179 2 56 58 14 167 243 43 80 123 19 113 190 31 40 71 16 115 299 29 73 102 15 78 121 45 34 80 12 118 137 25 72 97 18 87 305 4 42 46 16 173 157 31 61 93 17 2 96 -4 23 19 16 162 183 66 74 140 20 49 52 61 16 78 11 122 238 32 66 98 18 96 40 31 9 40 15 100 226 39 41 80 15 82 190 19 57 76 11 100 214 31 48 79 16 115 145 36 51 87 18 141 119 42 53 95 15 165 222 21 29 49 15 165 222 21 29 49 15 110 159 25 55 80 18 118 165 32 54 86 13 158 249 26 43 69 19 146 125 28 51 79 15 49 122 32 20 52 8 90 186 41 79 120 13 121 148 29 39 69 14 155 274 33 61 94 19 104 172 17 55 72 15 147 84 13 30 43 13 110 168 32 55 87 16 108 102 30 22 52 17 113 106 34 37 71 13 115 2 59 2 61 12 61 139 13 38 51 13 60 95 23 27 50 10 109 130 10 56 67 15 68 72 5 25 30 10 111 141 31 39 70 18 77 113 19 33 52 14 73 206 32 43 75 15 151 268 30 57 87 15 89 175 25 43 69 14 78 77 48 23 72 15 110 125 35 44 79 16 220 255 67 54 121 19 65 111 15 28 43 14 141 132 22 36 58 14 117 211 18 39 57 16 122 92 33 16 50 13 63 76 46 23 69 15 44 171 24 40 64 10 52 83 14 24 38 13 62 119 23 29 53 12 131 266 12 78 90 19 101 186 38 57 96 17 42 50 12 37 49 11 152 117 28 27 56 15 107 219 41 61 102 12 77 246 12 27 40 13 154 279 31 69 100 16 103 148 33 34 67 12 96 137 34 44 78 16 154 130 41 21 62 14 175 181 21 34 55 18 57 98 20 39 59 11 112 226 44 51 96 16 143 234 52 34 86 18 49 138 7 31 38 8 110 85 29 13 43 12 131 66 11 12 23 16 167 236 26 51 77 19 56 106 24 24 48 15 137 135 7 19 26 17 86 122 60 30 91 16 121 218 13 81 94 18 149 199 20 42 62 19 168 112 52 22 74 19 140 278 28 85 114 19 88 94 25 27 52 13 168 113 39 25 64 13 94 84 9 22 31 10 51 86 19 19 38 5 48 62 13 14 27 12 145 222 60 45 105 14 66 167 19 45 64 12 85 82 34 28 62 12 109 207 14 51 65 13 63 184 17 41 58 11 102 83 45 31 76 15 162 183 66 74 140 20 128 85 24 24 48 18 86 89 48 19 68 11 114 225 29 51 80 15 164 237 -2 73 71 18 119 102 51 24 76 14 126 221 2 61 63 16 132 128 24 23 46 13 142 91 40 14 53 12 83 198 20 54 74 12 94 204 19 51 70 15 81 158 16 62 78 15 166 138 20 36 56 13 110 226 40 59 100 17 64 44 27 24 51 8 93 196 25 26 52 8 104 83 49 54 102 13 105 79 39 39 78 8 49 52 61 16 78 11 88 105 19 36 55 12 95 116 67 31 98 10 102 83 45 31 76 15 99 196 30 42 73 17 63 153 8 39 47 13 76 157 19 25 45 14 109 75 52 31 83 16 117 106 22 38 60 17 57 58 17 31 48 11 120 75 33 17 50 15 73 74 34 22 56 12 91 185 22 55 77 15 108 265 30 62 91 18 105 131 25 51 76 15 117 139 38 30 68 15 119 196 26 49 74 17 31 78 13 16 29 8
Names of X columns:
LFM B PRH CH H Ex
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Do not include Seasonal Dummies
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
No Linear Trend
No Linear Trend
Linear Trend
First Differences
Seasonal Differences (s)
First and Seasonal Differences (s)
Degree of Predetermination (lagged endogenous variables)
Degree of Seasonal Predetermination
Seasonality
12
1
2
3
4
5
6
7
8
9
10
11
12
Chart options
R Code
library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,signif(mysum$coefficients[i,1],6)) a<-table.element(a, signif(mysum$coefficients[i,2],6)) a<-table.element(a, signif(mysum$coefficients[i,3],4)) a<-table.element(a, signif(mysum$coefficients[i,4],6)) a<-table.element(a, signif(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, signif(sqrt(mysum$r.squared),6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, signif(mysum$r.squared,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, signif(mysum$adj.r.squared,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[1],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[2],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[3],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, signif(mysum$sigma,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, signif(sum(myerror*myerror),6)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,signif(x[i],6)) a<-table.element(a,signif(x[i]-mysum$resid[i],6)) a<-table.element(a,signif(mysum$resid[i],6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6)) a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6)) a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,signif(numsignificant1,6)) a<-table.element(a,signif(numsignificant1/numgqtests,6)) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,signif(numsignificant5,6)) a<-table.element(a,signif(numsignificant5/numgqtests,6)) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,signif(numsignificant10,6)) a<-table.element(a,signif(numsignificant10/numgqtests,6)) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }
Compute
Summary of computational transaction
Raw Input
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Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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